用于最佳EFT灵敏度的参数化分类器
Parametrized classifiers for optimal EFT sensitivity
摘要：
我们研究未分组的多变量分析技术，统计学习的基础上，间接新的物理搜索在大型强子对撞机的有效场论框架。我们特别关注高能$ZW$生产与完全轻子衰变，在不同程度的细化到QCD的NLO建模。我们表明，一个相当大的增益的灵敏度是可能的，与目前的预测的基础上分箱分析。正如预期的那样，对于那些显示出与标准模型振幅干涉的复杂模式的操作员，增益尤其重要。最有效的方法被认为是“二次分类器”的方法，改进的标准统计学习分类器的微分截面上的EFT威尔逊系数的二次依赖是内置的，并纳入损失函数。我们认为，二次分类器的性能几乎是统计最优的，基于严格的最优性概念，我们可以建立一个近似的分析描述的$ZW$过程。
英文摘要：
We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy $ZW$ production with fully leptonic decays, modeled at different degrees of refinement up to NLO in QCD. We show that a considerable gain in sensitivity is possible compared with current projections based on binned analyses. As expected, the gain is particularly significant for those operators that display a complex pattern of interference with the Standard Model amplitude. The most effective method is found to be the "Quadratic Classifier" approach, an improvement of the standard Statistical Learning classifier where the quadratic dependence of the differential cross section on the EFT Wilson coefficients is built-in and incorporated in the loss function. We argue that the Quadratic Classifier performances are nearly statistically optimal, based on a rigorous notion of optimality that we can establish for an approximate analytic description of the $ZW$ process.
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